We will use the mean of water quality measurements.
ruisso_mean <- ruisso_sf3 %>%
group_by(sample_pts) %>%
summarise_at(vars(O2:Zn), mean, na.rm = TRUE)
ruisso_mean
## Simple feature collection with 50 features and 35 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -73.93704 ymin: 45.42516 xmax: -73.50467 ymax: 45.69734
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 50 x 36
## sample_pts O2 Conductivite pH Temperature COLI Ag Al
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAO-0.0 8.14 1180. 7.80 17.2 188. 0.100 295.
## 2 AAO-3.3P6 9.50 1378. 7.89 16.4 18599. 0.100 432.
## 3 AAO-3.5 8.79 1281. 7.74 14.3 612. 0.100 150.
## 4 AAO-3.6 9.00 1128. 7.97 16.1 461. 0.100 217.
## 5 AAO-6.4P12 10.1 1461. 7.96 18.2 147. 0.100 33.1
## 6 AAO-6.5 7.40 567. 8.12 16.5 1219. 0.100 109.
## 7 ADM-1 9.97 333. 8.19 21.2 58.6 0.100 119.
## 8 ANG-2 9.70 399. 8.33 20.2 41.0 0.100 60.7
## 9 BER-0.0 7.18 803. 7.61 17.1 305. 0.100 140.
## 10 BER-0.7P1 9.25 751. 7.76 17.1 1431. 0.100 75.7
## # ... with 40 more rows, and 28 more variables: As <dbl>, Ba <dbl>,
## # Be <dbl>, Ca <dbl>, Cd <dbl>, Co <dbl>, COT <dbl>, Cr <dbl>, Cu <dbl>,
## # Fe <dbl>, K <dbl>, Mg <dbl>, Mn <dbl>, Mo <dbl>, Na <dbl>, NH3 <dbl>,
## # Ni <dbl>, Ptot <dbl>, Pb <dbl>, MES <dbl>, Sb <dbl>, Se <dbl>,
## # Sn2 <dbl>, Tl <dbl>, U <dbl>, V <dbl>, Zn <dbl>, geometry <POINT [°]>